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4,325 results for “real”
Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)
<p>Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)</p>
Dataset for the Meet-URO 27 trial, paper "Real World Analysis of Peritoneal Metastasis From Renal Cell Carcinoma. Meet-Uro27"
<p>Dataset for the Meet-URO 27 trial, paper "Real World Analysis of Peritoneal Metastasis From Renal Cell Carcinoma. Meet-Uro27"</p>
Dataset for the ARON-2 trial, paper Global real-world experiences with pembrolizumab in advanced urothelial carcinoma after platinum-basedchemotherapy: the ARON-2 study.
<p>Dataset for the ARON-2 trial, paper: Global real-world experiences with pembrolizumab in advanced urothelial carcinoma after platinum-basedchemotherapy: the ARON-2 study.</p>
Mountain Rain Or Snow: Enhancing Avalanche Forecasting With Real-Time Precipitation Phase Data
Open the record for dataset details and reuse information.
3D Real-Time Single Particle Tracking using two-photon fluorescence from bright dye-based organic nanoparticles
<p>Data set, and attached ReadMe files related to the production of the figures in the article "3D Real-Time Single Particle Tracking using two-photon fluorescence from bright dye-based organic nanoparticles" by Emperauger et al.</p> <p> </p> <p>The ReadMe files explaining how each data file is organised, the data are in txt format, organised by figure and sub-figures and compressed. </p>
LLAMAFUZZ real-world programs supplementary experiment data
<p>This is the raw experimental data of LLAMAFUZZ and AFL++ on a set of real-world programs. We choose FuzzBench as running framework to ensure the fairness.</p>
Quantitative real-time PCR assays Q2 for species-specific detection and quantification of Baltic Sea spring bloom dinoflagellates
<p>These are the data behind figures 2 to 7 in the paper: Brink AM, Kremp A and Gorokhova E (2024) Quantitative real-time PCR assays for species-specific detection and quantification of Baltic Sea spring bloom dinoflagellates. Front. Microbiol. 15:1421101. doi: 10.3389/fmicb.2024.1421101</p>
Exploring agent interaction patterns in the comment sections of fake and real news
<p>The code and dataset provided for the paper <em>"Exploring agent interaction patterns in the comment sections of fake and real news"</em> contain all information about fake and real news, as well as the complete code for constructing comment networks and user network experiments.</p>
Exploring agent interaction patterns in the comment sections of fake and real news
<p>The code and dataset provided for the paper <em>"Exploring agent interaction patterns in the comment sections of fake and real news"</em> contain all information about fake and real news, as well as the complete code for constructing comment networks and user network experiments.</p>
Programmable optoelectronic Ising machine for optimization of real-world problems
<p>The data and code provided here are used to generate the figures in the manuscript titled "Programmable optoelectronic Ising machine for optimization of real-world problems" and its supplementary materials.</p>
The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling -- Supplementary Data
Open the record for dataset details and reuse information.
Synthetic and real imagery of extrusion from bioprinter
<p>Synthetic and real imagery of extrusion from bioprinter categorised into line and infill printing and further by extrusion characteristic (good, over, under). Taken from a sub build plate point of view.</p>
Barna & Seville real state dataset for PRA1 'Tipología y ciclo de vida de los datos' of UOC's Data Science Master
<p>Dataset containing the results of a web scraping process done in the portal Fotocasa for the cities of Barcelona and Seville (Spain), compiled in october and november 2024. under the framework of the subject "<em>Tipología y ciclo de vida de los datos (M2.851)</em>" of the Data Science Master in Universitat Oberta de Catalunya.</p>
Data for: Real-time Radial Tagging for Quantification of Left Ventricular Torsion
<p>Magnetic Resonance Imaging measurement and simulation data used in our research about ‘Real-time Radial Tagging for Quantification of Left Ventricular Torsion'.</p>
Data from: Detection of airborne genetically modified maize pollen by real-time PCR
The cultivation of genetically modified (GM) crops has raised in the European Union and other parts of the world numerous concerns about their environmental and economic impact. Especially outcrossing of genetically modified organisms (GMO) was from the beginning a critical issue as airborne pollen has been considered an important way of GMO dispersal. Here, we investigate the use of airborne pollen sampling combined with microscopic analysis and molecular PCR analysis as an approach to monitor GM maize cultivations in a specific area. Field trial experiments in the European Union and South America demonstrated the applicability of the approach under different climate conditions, in rural and semi-urban environment, even at very low levels of airborne pollen. The study documents in detail the sampling of GM pollen, sample DNA extraction and real-time PCR analysis. Our results suggest that this ''GM pollen monitoring by bioaerosol sampling and PCR screening" approach might represent an aid in managing the co-cultivation of GM and non-GM crops, especially in the surveillance of GM-free areas, centres of origin, and natural reserves.
Electrogenetic cellular insulin release for real-time glycemic control in type 1 diabetic mice
<p>Data underlying the figures in the publication “Electrogenetic cellular insulin release for real-time glycemic control in type 1 diabetic mice”, published in <em>Science</em>, <strong>2020</strong>, 368, 993-1001. <a href="https://science.sciencemag.org/content/368/6494/993">https://science.sciencemag.org/content/368/6494/993</a></p> <p>Table of contents:</p> <p><strong>1. Electrogenetics source data file</strong>; File openable with Graphpad Prism containing the source data for the main <em>Figures: 1-4 </em>(Transgene expression by SEAP measurement), <em>5b, 6a, 6f</em> (Insulin by ELISA), <em>5c, 6b, 6e </em>(NanoLuc luciferase), 7c (NanoLuc luciferase in vivo) and <em>7a, 7d</em> (Glycemia).</p> <p><strong>Transgene expression by SEAP measurement</strong></p> <p>Data for main <em>Figures 1, 2, 3, 4</em>.</p> <p>SEAP (human placental secreted alkaline phosphatase) levels were profiled in cell culture supernatants using a colorimetric assay. 100 µL 2x SEAP assay buffer (20 mM homoarginine, 1 mM MgCl2, 21% diethanolamine, pH 9.8) was mixed with 80 µL heat-inactivated (30 min at 65°C) cell culture supernatant. After the addition of 20 µL substrate solution (120 mM p-nitrophenyl phosphate; cat. no. AC128860100, Thermo Fisher Scientific), the absorbance time course was recorded for 45 min at 405 nm and 37°C using a Tecan Genios PRO plate reader (cat. no. P97084; Tecan Group AG, Maennedorf, Switzerland) and the SEAP levels were determined as follows: first, absorbance change over time (slope) was calculated. According to the Beer–Lambert’s law, absorbance is proportional to the concentration of a colored compound and depends on the light path length (d) and extinction coefficient (ε) (ε for p-nitrophenyl (εpNP) = 18.600 M−1 cm−1). Enzymatic activity EA [U/L] was calculated from the equation: EA = slope × dilution factor × εpNP−1 × d−1 </p> <p>Values in the file present determined SEAP levels.</p> <p><strong>Insulin by ELISA</strong></p> <p>Data for <em>Figures 5b, 6a, 6f</em>.</p> <p>Values in the file present Insulin level as determined by ELISA kit. The assay was performed according to manufacturer’s instructions.</p> <p><strong>NanoLuc luciferase </strong></p> <p>Data for <em>Figures 5c, 6b, 6e</em>.</p> <p>NanoLuc® luciferase was quantified in cell culture supernatants using the Nano-Glo® Luciferase Assay System (cat. no. N1110; Promega, Duebendorf, Switzerland). In brief, 7.5 µL of cell culture supernatant was added per well of a black 384-well plate and mixed with 7.5 µL substrate-containing assay buffer. Total luminescence was quantified using a Tecan Genios PRO plate reader (Tecan Group AG).</p> <p>Values in the file present measured luminescence levels.</p> <p><strong>NanoLuc luciferase in vivo </strong></p> <p>Data for <em>Figure 7c.</em></p> <p>Aliquots of 15 µL of whole-blood samples were diluted in 5 μL of 50 mM EDTA and frozen at -20 °C until NanoLuc® quantification as described above.</p> <p>Values in the file present measured luminescence levels normalized to time point 0 (Normalization individually for each mouse).</p> <p><strong>Glycemia </strong></p> <p>Data for <em>Figures 7a, 7d</em>.</p> <p>Blood glucose level was determined using a glucometer (Contour®Next, Bayer Healthcare, Leverkusen, Germany).</p> <p>Values in the file present measured glucose levels.</p> <p><strong>2. Figure 7b</strong>; Data for <em>Figure 7b</em>. File openable with Graphpad Prism.</p> <p>Blood glucose level was determined using a glucometer (Contour®Next, Bayer Healthcare, Leverkusen, Germany).</p> <p>Values in the file present measured glucose levels.</p> <p><strong>3. Figure 6c and 6d</strong>; Excel file with the data for <em>Figures 6c, 6d</em>.</p> <p>NanoLuc® luciferase was quantified in cell culture supernatants using the Nano-Glo® Luciferase Assay System (cat. no. N1110; Promega, Duebendorf, Switzerland). In brief, 7.5 µL of cell culture supernatant was added per well of a black 384-well plate and mixed with 7.5 µL substrate-containing assay buffer. Total luminescence was quantified using a Tecan Genios PRO plate reader (Tecan Group AG).</p> <p>Values in the file present measured luminescence levels.</p>
Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.
<p>Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers</p> <p><strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.</p> <p><strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+</p> <p><strong>Affiliations</strong>:</p> <p>1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy</p> <p>2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden</p> <p>3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden</p> <p>4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden</p> <p>5 Genevia technologies OY, Tampere, Finland</p> <p># these authors equally contributed</p> <p>+ Corresponding author</p> <p><strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy.</p> <p><strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.</p>
Real-space imaging of an anisotropic atomic charge of σ-hole by means of Kelvin probe force microscopy
<p>Data set for the publication with the title 'Real-space imaging of an anisotropic atomic charge of σ-hole by means of Kelvin probe force microscopy'.</p> <p>An anisotropic charge distribution on individual atoms, such as σ-holes, may strongly affect the material and structural properties of systems. However, the spatial resolution of such anisotropic charge distributions on an atom represents a long-standing experimental challenge. In particular, the existence of the σ-hole on halogen atoms has been demonstrated only indirectly through the determination of the crystal structures of organic molecules containing halogens or via theoretical calculations, but its direct experimental visualization has not yet been reported . Here we demonstrate that Kelvin probe force microscopy with a properly functionalized probe can image the anisotropic charge of the σ-hole and of a quadrupolar charge of a carbon monoxide molecule. This achievement opens a new way to characterize biological and chemical systems where anisotropic atomic charges play a decisive role.</p>
Figure 9. Mononchus truncatus Bastian, 1865 in Three new species of the genus Mononchus (Nematoda: Mononchida), and the "real" Mononchus truncatus Bastian, 1865
Figure 9. Mononchus truncatus Bastian, 1865. (A–C) Anterior end; (D, E) vulval region with two and four eggs, respectively; (F–H) female posterior end; (I) male posterior end; (J) cloacal region of male.
Figure 8. Mononchus truncatus Bastian, 1865 in Three new species of the genus Mononchus (Nematoda: Mononchida), and the "real" Mononchus truncatus Bastian, 1865
Figure 8. Mononchus truncatus Bastian, 1865. (A) Anterior end; (B) vulval region; (C, D) female posterior end; (E) cloacal region of male. Scale bars 20 µm each.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.